Medical imaging is a growing field as the technology available to create visual representations of the interior of a body continues to advance. Such visual representations may be used for clinical analysis and/or medical intervention, as they reveal internal structures that are hidden by skin and bones.
Many different imaging techniques may be used to image the cardiovascular system, such as echocardiography, cardiac computed-tomography (CT), and magnetic resonance imaging (MRI). One particular imaging method well-suited for the cardiovascular system is Diffusion MRI (dMRI), which uses the Brownian motion of water molecules to generate contrast in magnetic resonance images. dMRI allows for the mapping of the diffusion process of molecules in biological tissues. The water molecule diffusion patterns can reveal microscopic details about tissue architecture in the heart.
As with any imaging technique, there are challenges associated with missing or damaged data points when reconstructing organs based on dMRI data. Among the presently available techniques for recovering missing information in dMRI are the interpolation of the diffusion tensor or the diffusion signal itself, and the application of rule-based methods. Each known method has its drawbacks, particularly with regards to recovering fiber orientation in regions where the data are missing or have been corrupted.
Therefore, there is room for improvement.